{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图神经网络-gcn\n",
    "\n",
    "\n",
    "参考：\n",
    "\n",
    "矩阵运算方式的详细介绍\n",
    "\n",
    "https://blog.csdn.net/qq_43787862/article/details/113830925\n",
    "\n",
    "\n",
    "很详细，频谱原理，有矩阵运算的细节\n",
    "\n",
    "https://blog.csdn.net/yyl424525/article/details/100058264/\n",
    "\n",
    "\n",
    "pyg的官方课程有ppt与代码下载\n",
    "\n",
    "https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html\n",
    "\n",
    "\n",
    "实践代码略繁琐，有gcn底层代码讲解\n",
    "\n",
    "https://torture.blog.csdn.net/article/details/125287908\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  GCNConv的简单源码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GCNConv 简单的实现方式\n",
    "\n",
    "class GCNConv_s(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim, use_bias=True):\n",
    "\n",
    "        super(GCNConv_s, self).__init__()\n",
    "        self.input_dim = input_dim # 输入维度\n",
    "        self.output_dim = output_dim # 输出维度\n",
    "        self.use_bias = use_bias # 偏置\n",
    "        self.weight = nn.Parameter(torch.Tensor(input_dim, output_dim)) # 初始权重\n",
    "        if self.use_bias:\n",
    "            self.bias = nn.Parameter(torch.Tensor(output_dim)) # 偏置\n",
    "        else:\n",
    "            self.register_parameter('bias', None)\n",
    "        self.reset_parameters()\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        # 重新设置参数\n",
    "        # 进行凯明初始化\n",
    "        nn.init.kaiming_uniform_(self.weight)\n",
    "        if self.use_bias:\n",
    "            # 偏置先全给0\n",
    "            nn.init.zeros_(self.bias)\n",
    "\n",
    "    def forward(self, adjacency, input_feature,l=1):\n",
    "        '''\n",
    "\n",
    "        :param adjacency: 邻接矩阵\n",
    "        :param input_feature: 输入特征\n",
    "        :param l: lambda 影响自环权重值\n",
    "        :return:\n",
    "        '''\n",
    "        # 公式: (D^-0.5) A' (D^-0.5) X W\n",
    "        size = adjacency.shape[0]\n",
    "        # X W\n",
    "        support = torch.mm(input_feature, self.weight)\n",
    "\n",
    "        # A' = A + \\lambda I\n",
    "        A=adjacency+l*torch.eye(size)\n",
    "\n",
    "        # D: degree\n",
    "        SUM=A.sum(dim=1)\n",
    "        D=torch.diag_embed(SUM)\n",
    "        # D'=D^(-0.5)\n",
    "        D=D.__pow__(-0.5)\n",
    "        # 让inf值变成0\n",
    "        D[D==float(\"inf\")]=0\n",
    "\n",
    "        # (D^-0.5) A' (D^-0.5)\n",
    "        adjacency=torch.sparse.mm(D,adjacency)\n",
    "        adjacency=torch.sparse.mm(adjacency,D)\n",
    "        \n",
    "        # (D^-0.5) A' (D^-0.5) X W\n",
    "        output = torch.sparse.mm(adjacency, support)\n",
    "        \n",
    "        if self.use_bias:\n",
    "            # 使用偏置\n",
    "            output += self.bias\n",
    "        return output\n",
    "\n",
    "    def __repr__(self):\n",
    "        # 打印的时候内存信息属性\n",
    "        return self.__class__.__name__ + ' (' + str(self.input_dim) + ' -> ' + str(self.output_dim) + ')'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cora数据集与点分类任务\n",
    "\n",
    "Cora数据集，该数据集由 2708 篇论文，及它们之间的引用关系构成的 5429 条边组成。\n",
    "这些论文被根据主题划分为7类，分别是神经网络、强化学习、规则学习、概率方法、遗传算法、理论研究、案例相关。\n",
    "每篇论文的特征是通过词袋模型得到的，维度为1433，每一维表示一个词，1表示该词在这篇文章中出现过，0表示未出现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.datasets import Planetoid\n",
    "from torch_geometric.utils import to_networkx\n",
    "import networkx as nx\n",
    "from torch.nn import Linear\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "网络数据包含的类数量: 7\n",
      "网络数据边的特征数量: 0\n",
      "网络数据边的数量: 5278.0\n",
      "网络数据节点的特征数量: 1433\n",
      "网络数据节点的数量: 2708\n"
     ]
    }
   ],
   "source": [
    "# 导入Cora数据集\n",
    "dataset=Planetoid(root=r\"./data\",name=\"Cora\") # root: 指定路径 name: 数据集名称\n",
    "# 查看数据的基本情况\n",
    "print(\"网络数据包含的类数量:\",dataset.num_classes)\n",
    "print(\"网络数据边的特征数量:\",dataset.num_edge_features)\n",
    "print(\"网络数据边的数量:\",dataset.edge_index.shape[1]/2) # 除以2是OOC的组织形式\n",
    "print(\"网络数据节点的特征数量:\",dataset.num_node_features)\n",
    "print(\"网络数据节点的数量:\",dataset.x.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2708"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataset.edge_index\n",
    "len(dataset.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataset.data\n",
    "dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ True,  True,  True,  ..., False, False, False])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0].train_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "网络的边的数量:  5278\n",
      "网络的节点的数量:  2708\n",
      "节点分类: [3 4 4 ... 3 3 3]\n"
     ]
    }
   ],
   "source": [
    "# 通过networkx库进行可视化\n",
    "CoraNet=to_networkx(dataset[0]) # type : networkx.classes.graph.Graph\n",
    "CoraNet=CoraNet.to_undirected() # 转化为无向图\n",
    "\n",
    "# 查看网络情况\n",
    "print(\"网络的边的数量: \",CoraNet.number_of_edges())\n",
    "print(\"网络的节点的数量: \",CoraNet.number_of_nodes())\n",
    "Node_class=dataset.y.data.numpy()\n",
    "print(\"节点分类:\",Node_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nx图结构可视化\n",
    "pos=nx.spring_layout(CoraNet) # 网络图中节点的布局方式\n",
    "nodecolor=[\"red\",\"blue\",\"green\",\"yellow\",\"peru\",\"violet\",\"cyan\"]\n",
    "nodelabel=np.array(list(CoraNet.nodes))\n",
    "plt.figure(figsize=(16,12))\n",
    "\n",
    "# 不同的类使用不同的颜色\n",
    "for ii in np.arange(len(np.unique(Node_class))):\n",
    "    nodelist=nodelabel[Node_class==ii]\n",
    "    # 绘制节点\n",
    "    nx.draw_networkx_nodes(CoraNet,pos,nodelist=list(nodelist),node_size=20,\n",
    "                           node_color=nodecolor[ii],alpha=0.8)\n",
    "\n",
    "# 为网络添加边\n",
    "nx.draw_networkx_edges(CoraNet,pos,width=1,edge_color=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 传统神经网络的实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP(\n",
      "  (lin1): Linear(in_features=1433, out_features=16, bias=True)\n",
      "  (lin2): Linear(in_features=16, out_features=7, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class MLP(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super().__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.lin1 = Linear(dataset.num_features, hidden_channels)\n",
    "        self.lin2 = Linear(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.lin1(x)\n",
    "        x = x.relu()\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0 , loss:1.9619522094726562, accracy:0.1\n",
      "epoch:1 , loss:1.9027512073516846, accracy:0.108\n",
      "epoch:2 , loss:1.8414477109909058, accracy:0.14\n",
      "epoch:3 , loss:1.7561613321304321, accracy:0.166\n",
      "epoch:4 , loss:1.6390374898910522, accracy:0.223\n",
      "epoch:5 , loss:1.5613676309585571, accracy:0.256\n",
      "epoch:6 , loss:1.4281829595565796, accracy:0.278\n",
      "epoch:7 , loss:1.3505451679229736, accracy:0.308\n",
      "epoch:8 , loss:1.2641708850860596, accracy:0.336\n",
      "epoch:9 , loss:1.1786242723464966, accracy:0.331\n",
      "epoch:10 , loss:1.0886567831039429, accracy:0.315\n",
      "epoch:11 , loss:0.9625499248504639, accracy:0.336\n",
      "epoch:12 , loss:0.9238315224647522, accracy:0.355\n",
      "epoch:13 , loss:0.8176745772361755, accracy:0.356\n",
      "epoch:14 , loss:0.7710466384887695, accracy:0.366\n",
      "epoch:15 , loss:0.6765591502189636, accracy:0.376\n",
      "epoch:16 , loss:0.6483007073402405, accracy:0.376\n",
      "epoch:17 , loss:0.5991811752319336, accracy:0.37\n",
      "epoch:18 , loss:0.5321784019470215, accracy:0.36\n",
      "epoch:19 , loss:0.5321124196052551, accracy:0.342\n",
      "epoch:20 , loss:0.4860765039920807, accracy:0.357\n",
      "epoch:21 , loss:0.5025644898414612, accracy:0.376\n",
      "epoch:22 , loss:0.4680023193359375, accracy:0.369\n",
      "epoch:23 , loss:0.42764607071876526, accracy:0.362\n",
      "epoch:24 , loss:0.3171105980873108, accracy:0.372\n",
      "epoch:25 , loss:0.3860819935798645, accracy:0.399\n",
      "epoch:26 , loss:0.34289148449897766, accracy:0.38\n",
      "epoch:27 , loss:0.3348960876464844, accracy:0.361\n",
      "epoch:28 , loss:0.32896238565444946, accracy:0.373\n",
      "epoch:29 , loss:0.33805546164512634, accracy:0.369\n",
      "epoch:30 , loss:0.34936097264289856, accracy:0.402\n",
      "epoch:31 , loss:0.25941362977027893, accracy:0.374\n",
      "epoch:32 , loss:0.32629188895225525, accracy:0.405\n",
      "epoch:33 , loss:0.26671430468559265, accracy:0.394\n",
      "epoch:34 , loss:0.324049174785614, accracy:0.375\n",
      "epoch:35 , loss:0.35762104392051697, accracy:0.389\n",
      "epoch:36 , loss:0.34466710686683655, accracy:0.402\n",
      "epoch:37 , loss:0.2686784863471985, accracy:0.393\n",
      "epoch:38 , loss:0.26095080375671387, accracy:0.392\n",
      "epoch:39 , loss:0.24715176224708557, accracy:0.415\n",
      "epoch:40 , loss:0.2562868893146515, accracy:0.415\n",
      "epoch:41 , loss:0.27407306432724, accracy:0.396\n",
      "epoch:42 , loss:0.2603539228439331, accracy:0.379\n",
      "epoch:43 , loss:0.24453838169574738, accracy:0.379\n",
      "epoch:44 , loss:0.2573055326938629, accracy:0.385\n",
      "epoch:45 , loss:0.22938308119773865, accracy:0.401\n",
      "epoch:46 , loss:0.2766801416873932, accracy:0.41\n",
      "epoch:47 , loss:0.19075104594230652, accracy:0.406\n",
      "epoch:48 , loss:0.19751222431659698, accracy:0.415\n",
      "epoch:49 , loss:0.23911644518375397, accracy:0.403\n",
      "epoch:50 , loss:0.23105059564113617, accracy:0.377\n",
      "epoch:51 , loss:0.3435773551464081, accracy:0.38\n",
      "epoch:52 , loss:0.17709918320178986, accracy:0.395\n",
      "epoch:53 , loss:0.25807440280914307, accracy:0.397\n",
      "epoch:54 , loss:0.16815748810768127, accracy:0.388\n",
      "epoch:55 , loss:0.21766239404678345, accracy:0.395\n",
      "epoch:56 , loss:0.2599666118621826, accracy:0.406\n",
      "epoch:57 , loss:0.22469006478786469, accracy:0.415\n",
      "epoch:58 , loss:0.22544725239276886, accracy:0.406\n",
      "epoch:59 , loss:0.23412641882896423, accracy:0.417\n",
      "epoch:60 , loss:0.23524066805839539, accracy:0.399\n",
      "epoch:61 , loss:0.22830066084861755, accracy:0.421\n",
      "epoch:62 , loss:0.19699257612228394, accracy:0.4\n",
      "epoch:63 , loss:0.23517391085624695, accracy:0.398\n",
      "epoch:64 , loss:0.17331340909004211, accracy:0.393\n",
      "epoch:65 , loss:0.15904663503170013, accracy:0.393\n",
      "epoch:66 , loss:0.22824852168560028, accracy:0.381\n",
      "epoch:67 , loss:0.14989478886127472, accracy:0.407\n",
      "epoch:68 , loss:0.20119282603263855, accracy:0.391\n",
      "epoch:69 , loss:0.18546581268310547, accracy:0.369\n",
      "epoch:70 , loss:0.26051050424575806, accracy:0.405\n",
      "epoch:71 , loss:0.22774815559387207, accracy:0.387\n",
      "epoch:72 , loss:0.24707132577896118, accracy:0.397\n",
      "epoch:73 , loss:0.22542403638362885, accracy:0.401\n",
      "epoch:74 , loss:0.2283591479063034, accracy:0.381\n",
      "epoch:75 , loss:0.17617779970169067, accracy:0.406\n",
      "epoch:76 , loss:0.20477508008480072, accracy:0.386\n",
      "epoch:77 , loss:0.13678309321403503, accracy:0.385\n",
      "epoch:78 , loss:0.15460041165351868, accracy:0.392\n",
      "epoch:79 , loss:0.2257813811302185, accracy:0.412\n",
      "epoch:80 , loss:0.2071162909269333, accracy:0.409\n",
      "epoch:81 , loss:0.2735007405281067, accracy:0.427\n",
      "epoch:82 , loss:0.22484508156776428, accracy:0.396\n",
      "epoch:83 , loss:0.2115212231874466, accracy:0.402\n",
      "epoch:84 , loss:0.2549319267272949, accracy:0.411\n",
      "epoch:85 , loss:0.1166185662150383, accracy:0.398\n",
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      "epoch:866 , loss:0.12762407958507538, accracy:0.461\n",
      "epoch:867 , loss:0.19554871320724487, accracy:0.473\n",
      "epoch:868 , loss:0.14356856048107147, accracy:0.46\n",
      "epoch:869 , loss:0.1433441936969757, accracy:0.456\n",
      "epoch:870 , loss:0.13974513113498688, accracy:0.454\n",
      "epoch:871 , loss:0.1284472644329071, accracy:0.466\n",
      "epoch:872 , loss:0.14083819091320038, accracy:0.442\n",
      "epoch:873 , loss:0.10148096084594727, accracy:0.453\n",
      "epoch:874 , loss:0.1646081507205963, accracy:0.475\n",
      "epoch:875 , loss:0.16572611033916473, accracy:0.489\n",
      "epoch:876 , loss:0.15920893847942352, accracy:0.457\n",
      "epoch:877 , loss:0.11400792747735977, accracy:0.463\n",
      "epoch:878 , loss:0.196529358625412, accracy:0.491\n",
      "epoch:879 , loss:0.12942162156105042, accracy:0.491\n",
      "epoch:880 , loss:0.1838025599718094, accracy:0.469\n",
      "epoch:881 , loss:0.14351581037044525, accracy:0.469\n",
      "epoch:882 , loss:0.14180251955986023, accracy:0.467\n",
      "epoch:883 , loss:0.167012020945549, accracy:0.47\n",
      "epoch:884 , loss:0.13456645607948303, accracy:0.469\n",
      "epoch:885 , loss:0.15805788338184357, accracy:0.464\n",
      "epoch:886 , loss:0.18640649318695068, accracy:0.473\n",
      "epoch:887 , loss:0.17504023015499115, accracy:0.466\n",
      "epoch:888 , loss:0.13684742152690887, accracy:0.462\n",
      "epoch:889 , loss:0.1920672208070755, accracy:0.473\n",
      "epoch:890 , loss:0.11402014642953873, accracy:0.476\n",
      "epoch:891 , loss:0.14580228924751282, accracy:0.452\n",
      "epoch:892 , loss:0.1591366082429886, accracy:0.448\n",
      "epoch:893 , loss:0.20876802504062653, accracy:0.471\n",
      "epoch:894 , loss:0.15769708156585693, accracy:0.468\n",
      "epoch:895 , loss:0.18037356436252594, accracy:0.488\n",
      "epoch:896 , loss:0.1650858074426651, accracy:0.476\n",
      "epoch:897 , loss:0.23003733158111572, accracy:0.468\n",
      "epoch:898 , loss:0.13812455534934998, accracy:0.459\n",
      "epoch:899 , loss:0.12951530516147614, accracy:0.472\n",
      "epoch:900 , loss:0.18875932693481445, accracy:0.474\n",
      "epoch:901 , loss:0.13393037021160126, accracy:0.458\n",
      "epoch:902 , loss:0.1513088047504425, accracy:0.454\n",
      "epoch:903 , loss:0.16497711837291718, accracy:0.467\n",
      "epoch:904 , loss:0.14383035898208618, accracy:0.481\n",
      "epoch:905 , loss:0.16308559477329254, accracy:0.473\n",
      "epoch:906 , loss:0.10818684101104736, accracy:0.477\n",
      "epoch:907 , loss:0.14902231097221375, accracy:0.488\n",
      "epoch:908 , loss:0.16838495433330536, accracy:0.467\n",
      "epoch:909 , loss:0.1447725147008896, accracy:0.461\n",
      "epoch:910 , loss:0.15340358018875122, accracy:0.478\n",
      "epoch:911 , loss:0.1812094897031784, accracy:0.463\n",
      "epoch:912 , loss:0.11840910464525223, accracy:0.454\n",
      "epoch:913 , loss:0.18991169333457947, accracy:0.475\n",
      "epoch:914 , loss:0.1515953093767166, accracy:0.486\n",
      "epoch:915 , loss:0.1345885545015335, accracy:0.453\n",
      "epoch:916 , loss:0.12970565259456635, accracy:0.463\n",
      "epoch:917 , loss:0.12821625173091888, accracy:0.476\n",
      "epoch:918 , loss:0.13569991290569305, accracy:0.464\n",
      "epoch:919 , loss:0.18708035349845886, accracy:0.44\n",
      "epoch:920 , loss:0.21844156086444855, accracy:0.463\n",
      "epoch:921 , loss:0.16062426567077637, accracy:0.442\n",
      "epoch:922 , loss:0.09625037759542465, accracy:0.483\n",
      "epoch:923 , loss:0.19628457725048065, accracy:0.459\n",
      "epoch:924 , loss:0.1448979526758194, accracy:0.487\n",
      "epoch:925 , loss:0.09876370429992676, accracy:0.458\n",
      "epoch:926 , loss:0.13153713941574097, accracy:0.459\n",
      "epoch:927 , loss:0.1701577603816986, accracy:0.463\n",
      "epoch:928 , loss:0.12252988666296005, accracy:0.466\n",
      "epoch:929 , loss:0.1317359209060669, accracy:0.494\n",
      "epoch:930 , loss:0.16358822584152222, accracy:0.469\n",
      "epoch:931 , loss:0.12625068426132202, accracy:0.483\n",
      "epoch:932 , loss:0.12410780042409897, accracy:0.453\n",
      "epoch:933 , loss:0.16079741716384888, accracy:0.483\n",
      "epoch:934 , loss:0.11502086371183395, accracy:0.469\n",
      "epoch:935 , loss:0.19125443696975708, accracy:0.453\n",
      "epoch:936 , loss:0.15672142803668976, accracy:0.494\n",
      "epoch:937 , loss:0.1426870971918106, accracy:0.456\n",
      "epoch:938 , loss:0.1840880811214447, accracy:0.481\n",
      "epoch:939 , loss:0.1747671216726303, accracy:0.474\n",
      "epoch:940 , loss:0.1804691106081009, accracy:0.475\n",
      "epoch:941 , loss:0.15527869760990143, accracy:0.487\n",
      "epoch:942 , loss:0.13778026401996613, accracy:0.464\n",
      "epoch:943 , loss:0.23846405744552612, accracy:0.48\n",
      "epoch:944 , loss:0.2052707076072693, accracy:0.444\n",
      "epoch:945 , loss:0.1662597507238388, accracy:0.448\n",
      "epoch:946 , loss:0.16097839176654816, accracy:0.45\n",
      "epoch:947 , loss:0.13413181900978088, accracy:0.473\n",
      "epoch:948 , loss:0.16454707086086273, accracy:0.442\n",
      "epoch:949 , loss:0.14693361520767212, accracy:0.456\n",
      "epoch:950 , loss:0.16763043403625488, accracy:0.479\n",
      "epoch:951 , loss:0.1768265813589096, accracy:0.475\n",
      "epoch:952 , loss:0.18496161699295044, accracy:0.469\n",
      "epoch:953 , loss:0.2122287005186081, accracy:0.47\n",
      "epoch:954 , loss:0.08458105474710464, accracy:0.453\n",
      "epoch:955 , loss:0.19619610905647278, accracy:0.465\n",
      "epoch:956 , loss:0.1688040941953659, accracy:0.477\n",
      "epoch:957 , loss:0.11880713701248169, accracy:0.466\n",
      "epoch:958 , loss:0.2179701179265976, accracy:0.469\n",
      "epoch:959 , loss:0.11021735519170761, accracy:0.456\n",
      "epoch:960 , loss:0.09567428380250931, accracy:0.467\n",
      "epoch:961 , loss:0.18154089152812958, accracy:0.474\n",
      "epoch:962 , loss:0.15897834300994873, accracy:0.478\n",
      "epoch:963 , loss:0.16904526948928833, accracy:0.466\n",
      "epoch:964 , loss:0.09650105983018875, accracy:0.462\n",
      "epoch:965 , loss:0.1293836385011673, accracy:0.467\n",
      "epoch:966 , loss:0.1316184401512146, accracy:0.444\n",
      "epoch:967 , loss:0.13096868991851807, accracy:0.477\n",
      "epoch:968 , loss:0.16031011939048767, accracy:0.478\n",
      "epoch:969 , loss:0.14607787132263184, accracy:0.467\n",
      "epoch:970 , loss:0.16573309898376465, accracy:0.452\n",
      "epoch:971 , loss:0.15920445322990417, accracy:0.455\n",
      "epoch:972 , loss:0.179151251912117, accracy:0.447\n",
      "epoch:973 , loss:0.15852679312229156, accracy:0.47\n",
      "epoch:974 , loss:0.1195552796125412, accracy:0.481\n",
      "epoch:975 , loss:0.17912325263023376, accracy:0.478\n",
      "epoch:976 , loss:0.20883645117282867, accracy:0.468\n",
      "epoch:977 , loss:0.1590443104505539, accracy:0.481\n",
      "epoch:978 , loss:0.16796764731407166, accracy:0.459\n",
      "epoch:979 , loss:0.15682463347911835, accracy:0.469\n",
      "epoch:980 , loss:0.1274905949831009, accracy:0.459\n",
      "epoch:981 , loss:0.10704993456602097, accracy:0.457\n",
      "epoch:982 , loss:0.13089044392108917, accracy:0.459\n",
      "epoch:983 , loss:0.164542555809021, accracy:0.467\n",
      "epoch:984 , loss:0.15800897777080536, accracy:0.485\n",
      "epoch:985 , loss:0.09956444799900055, accracy:0.471\n",
      "epoch:986 , loss:0.15301235020160675, accracy:0.453\n",
      "epoch:987 , loss:0.1562052220106125, accracy:0.488\n",
      "epoch:988 , loss:0.1839366853237152, accracy:0.469\n",
      "epoch:989 , loss:0.10996492207050323, accracy:0.477\n",
      "epoch:990 , loss:0.11986108869314194, accracy:0.453\n",
      "epoch:991 , loss:0.14539344608783722, accracy:0.453\n",
      "epoch:992 , loss:0.10698776692152023, accracy:0.479\n",
      "epoch:993 , loss:0.1275150328874588, accracy:0.478\n",
      "epoch:994 , loss:0.11107958853244781, accracy:0.474\n",
      "epoch:995 , loss:0.16972368955612183, accracy:0.444\n",
      "epoch:996 , loss:0.1548958718776703, accracy:0.491\n",
      "epoch:997 , loss:0.12222127616405487, accracy:0.453\n",
      "epoch:998 , loss:0.16865243017673492, accracy:0.468\n",
      "epoch:999 , loss:0.1505894660949707, accracy:0.502\n"
     ]
    }
   ],
   "source": [
    "model = MLP(hidden_channels=16)\n",
    "criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\n",
    "opt = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)  # Define optimizer.\n",
    "data=dataset[0]\n",
    "\n",
    "for i in range(1000):\n",
    "    out = model.forward(data.x)\n",
    "    loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
    "    opt.zero_grad()\n",
    "    loss.backward()\n",
    "    opt.step()\n",
    "\n",
    "    pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "    test_correct = pred[data.test_mask] == data.y[data.test_mask]  # Check against ground-truth labels.\n",
    "    test_acc = int(test_correct.sum()) / int(data.test_mask.sum())  # Derive ratio of correct predictions.\n",
    "\n",
    "    print(\"epoch:{} , loss:{}, accracy:{}\".format(i,loss,test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图神经网络的实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): GCNConv(1433, 16)\n",
      "  (conv2): GCNConv(16, 7)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super().__init__()\n",
    "        torch.manual_seed(1234567)\n",
    "        self.conv1 = GCNConv(dataset.num_features, hidden_channels)\n",
    "        self.conv2 = GCNConv(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.conv2(x, edge_index)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0 , loss:1.9512755870819092, accracy:0.095\n",
      "epoch:1 , loss:1.8534587621688843, accracy:0.204\n",
      "epoch:2 , loss:1.7390815019607544, accracy:0.243\n",
      "epoch:3 , loss:1.6211824417114258, accracy:0.288\n",
      "epoch:4 , loss:1.461644172668457, accracy:0.347\n",
      "epoch:5 , loss:1.333328127861023, accracy:0.431\n",
      "epoch:6 , loss:1.209903359413147, accracy:0.518\n",
      "epoch:7 , loss:1.1018539667129517, accracy:0.578\n",
      "epoch:8 , loss:0.9800645112991333, accracy:0.59\n",
      "epoch:9 , loss:0.8722946047782898, accracy:0.612\n",
      "epoch:10 , loss:0.7703157663345337, accracy:0.654\n",
      "epoch:11 , loss:0.684578537940979, accracy:0.632\n",
      "epoch:12 , loss:0.5754106640815735, accracy:0.648\n",
      "epoch:13 , loss:0.5386150479316711, accracy:0.674\n",
      "epoch:14 , loss:0.4516831040382385, accracy:0.697\n",
      "epoch:15 , loss:0.4167480170726776, accracy:0.683\n",
      "epoch:16 , loss:0.3837454319000244, accracy:0.701\n",
      "epoch:17 , loss:0.32034093141555786, accracy:0.682\n",
      "epoch:18 , loss:0.2962862551212311, accracy:0.714\n",
      "epoch:19 , loss:0.23860329389572144, accracy:0.72\n",
      "epoch:20 , loss:0.2300247997045517, accracy:0.732\n",
      "epoch:21 , loss:0.21812841296195984, accracy:0.724\n",
      "epoch:22 , loss:0.2071177363395691, accracy:0.73\n",
      "epoch:23 , loss:0.1662331372499466, accracy:0.725\n",
      "epoch:24 , loss:0.19377334415912628, accracy:0.732\n",
      "epoch:25 , loss:0.15030206739902496, accracy:0.733\n",
      "epoch:26 , loss:0.13592301309108734, accracy:0.718\n",
      "epoch:27 , loss:0.14126642048358917, accracy:0.739\n",
      "epoch:28 , loss:0.1157723143696785, accracy:0.706\n",
      "epoch:29 , loss:0.11190187931060791, accracy:0.736\n",
      "epoch:30 , loss:0.10133252292871475, accracy:0.725\n",
      "epoch:31 , loss:0.10677110403776169, accracy:0.714\n",
      "epoch:32 , loss:0.0903337150812149, accracy:0.728\n",
      "epoch:33 , loss:0.1104782372713089, accracy:0.728\n",
      "epoch:34 , loss:0.0873347818851471, accracy:0.741\n",
      "epoch:35 , loss:0.06690498441457748, accracy:0.74\n",
      "epoch:36 , loss:0.057844072580337524, accracy:0.734\n",
      "epoch:37 , loss:0.062130287289619446, accracy:0.759\n",
      "epoch:38 , loss:0.05941472202539444, accracy:0.725\n",
      "epoch:39 , loss:0.057500600814819336, accracy:0.756\n",
      "epoch:40 , loss:0.047695986926555634, accracy:0.742\n",
      "epoch:41 , loss:0.054288074374198914, accracy:0.749\n",
      "epoch:42 , loss:0.063601054251194, accracy:0.773\n",
      "epoch:43 , loss:0.04724251478910446, accracy:0.754\n",
      "epoch:44 , loss:0.06719477474689484, accracy:0.756\n",
      "epoch:45 , loss:0.056953124701976776, accracy:0.76\n",
      "epoch:46 , loss:0.06918460130691528, accracy:0.746\n",
      "epoch:47 , loss:0.05654340609908104, accracy:0.729\n",
      "epoch:48 , loss:0.0506223663687706, accracy:0.759\n",
      "epoch:49 , loss:0.04855940490961075, accracy:0.744\n",
      "epoch:50 , loss:0.05872834101319313, accracy:0.741\n",
      "epoch:51 , loss:0.05144023895263672, accracy:0.742\n",
      "epoch:52 , loss:0.04204221069812775, accracy:0.745\n",
      "epoch:53 , loss:0.033720456063747406, accracy:0.744\n",
      "epoch:54 , loss:0.03756054863333702, accracy:0.735\n",
      "epoch:55 , loss:0.06066697835922241, accracy:0.722\n",
      "epoch:56 , loss:0.045114915817976, accracy:0.72\n",
      "epoch:57 , loss:0.0508093535900116, accracy:0.732\n",
      "epoch:58 , loss:0.04274965450167656, accracy:0.723\n",
      "epoch:59 , loss:0.06448684632778168, accracy:0.748\n",
      "epoch:60 , loss:0.05408210679888725, accracy:0.737\n",
      "epoch:61 , loss:0.03612367808818817, accracy:0.745\n",
      "epoch:62 , loss:0.06010889261960983, accracy:0.731\n",
      "epoch:63 , loss:0.05610833689570427, accracy:0.742\n",
      "epoch:64 , loss:0.03682122007012367, accracy:0.736\n",
      "epoch:65 , loss:0.045212823897600174, accracy:0.737\n",
      "epoch:66 , loss:0.03746629133820534, accracy:0.753\n",
      "epoch:67 , loss:0.0352264940738678, accracy:0.738\n",
      "epoch:68 , loss:0.07861747592687607, accracy:0.736\n",
      "epoch:69 , loss:0.034275781363248825, accracy:0.769\n",
      "epoch:70 , loss:0.045786820352077484, accracy:0.744\n",
      "epoch:71 , loss:0.0425659604370594, accracy:0.748\n",
      "epoch:72 , loss:0.03828447684645653, accracy:0.748\n",
      "epoch:73 , loss:0.04691294580698013, accracy:0.737\n",
      "epoch:74 , loss:0.050974346697330475, accracy:0.765\n",
      "epoch:75 , loss:0.03017079085111618, accracy:0.77\n",
      "epoch:76 , loss:0.051286064088344574, accracy:0.759\n",
      "epoch:77 , loss:0.041104815900325775, accracy:0.748\n",
      "epoch:78 , loss:0.033780720084905624, accracy:0.738\n",
      "epoch:79 , loss:0.03410038724541664, accracy:0.753\n",
      "epoch:80 , loss:0.03918631002306938, accracy:0.746\n",
      "epoch:81 , loss:0.035639483481645584, accracy:0.757\n",
      "epoch:82 , loss:0.04649871587753296, accracy:0.731\n",
      "epoch:83 , loss:0.03588259592652321, accracy:0.732\n",
      "epoch:84 , loss:0.033343780785799026, accracy:0.758\n",
      "epoch:85 , loss:0.04567132890224457, accracy:0.739\n",
      "epoch:86 , loss:0.039735741913318634, accracy:0.738\n",
      "epoch:87 , loss:0.04915180057287216, accracy:0.734\n",
      "epoch:88 , loss:0.04068831354379654, accracy:0.746\n",
      "epoch:89 , loss:0.03994634374976158, accracy:0.736\n",
      "epoch:90 , loss:0.04506860673427582, accracy:0.742\n",
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      "epoch:906 , loss:0.01982102170586586, accracy:0.743\n",
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      "epoch:909 , loss:0.01960117742419243, accracy:0.757\n",
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      "epoch:915 , loss:0.011267325840890408, accracy:0.771\n",
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      "epoch:918 , loss:0.024009130895137787, accracy:0.776\n",
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      "epoch:920 , loss:0.01954818330705166, accracy:0.749\n",
      "epoch:921 , loss:0.015586869791150093, accracy:0.765\n",
      "epoch:922 , loss:0.029910219833254814, accracy:0.77\n",
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      "epoch:924 , loss:0.022993262857198715, accracy:0.765\n",
      "epoch:925 , loss:0.01563146896660328, accracy:0.743\n",
      "epoch:926 , loss:0.011577831581234932, accracy:0.758\n",
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      "epoch:931 , loss:0.009793110191822052, accracy:0.775\n",
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      "epoch:933 , loss:0.01829386129975319, accracy:0.752\n",
      "epoch:934 , loss:0.00831031333655119, accracy:0.767\n",
      "epoch:935 , loss:0.019338874146342278, accracy:0.746\n",
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      "epoch:940 , loss:0.017888272181153297, accracy:0.764\n",
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      "epoch:949 , loss:0.013292537070810795, accracy:0.765\n",
      "epoch:950 , loss:0.025632157921791077, accracy:0.77\n",
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      "epoch:954 , loss:0.011449151672422886, accracy:0.763\n",
      "epoch:955 , loss:0.023890746757388115, accracy:0.768\n",
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      "epoch:957 , loss:0.007524304091930389, accracy:0.756\n",
      "epoch:958 , loss:0.015837641432881355, accracy:0.758\n",
      "epoch:959 , loss:0.013257347978651524, accracy:0.744\n",
      "epoch:960 , loss:0.02669164165854454, accracy:0.767\n",
      "epoch:961 , loss:0.015372214838862419, accracy:0.744\n",
      "epoch:962 , loss:0.021580979228019714, accracy:0.76\n",
      "epoch:963 , loss:0.023705242201685905, accracy:0.749\n",
      "epoch:964 , loss:0.015894640237092972, accracy:0.739\n",
      "epoch:965 , loss:0.014686567708849907, accracy:0.765\n",
      "epoch:966 , loss:0.018265986815094948, accracy:0.753\n",
      "epoch:967 , loss:0.021374521777033806, accracy:0.755\n",
      "epoch:968 , loss:0.01772828958928585, accracy:0.755\n",
      "epoch:969 , loss:0.01893772929906845, accracy:0.766\n",
      "epoch:970 , loss:0.01684846356511116, accracy:0.738\n",
      "epoch:971 , loss:0.015340443700551987, accracy:0.759\n",
      "epoch:972 , loss:0.027356907725334167, accracy:0.751\n",
      "epoch:973 , loss:0.014085832983255386, accracy:0.758\n",
      "epoch:974 , loss:0.02041187323629856, accracy:0.752\n",
      "epoch:975 , loss:0.010359193198382854, accracy:0.767\n",
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      "epoch:977 , loss:0.011830396018922329, accracy:0.758\n",
      "epoch:978 , loss:0.018961280584335327, accracy:0.757\n",
      "epoch:979 , loss:0.01468623522669077, accracy:0.746\n",
      "epoch:980 , loss:0.015784354880452156, accracy:0.766\n",
      "epoch:981 , loss:0.015134324319660664, accracy:0.754\n",
      "epoch:982 , loss:0.03451725095510483, accracy:0.751\n",
      "epoch:983 , loss:0.022128215059638023, accracy:0.767\n",
      "epoch:984 , loss:0.022175738587975502, accracy:0.763\n",
      "epoch:985 , loss:0.023278705775737762, accracy:0.75\n",
      "epoch:986 , loss:0.01942140981554985, accracy:0.766\n",
      "epoch:987 , loss:0.02111705392599106, accracy:0.766\n",
      "epoch:988 , loss:0.023348083719611168, accracy:0.769\n",
      "epoch:989 , loss:0.009543459862470627, accracy:0.764\n",
      "epoch:990 , loss:0.027380183339118958, accracy:0.767\n",
      "epoch:991 , loss:0.014108427800238132, accracy:0.763\n",
      "epoch:992 , loss:0.016239456832408905, accracy:0.765\n",
      "epoch:993 , loss:0.013188890181481838, accracy:0.771\n",
      "epoch:994 , loss:0.012175403535366058, accracy:0.774\n",
      "epoch:995 , loss:0.01403076108545065, accracy:0.756\n",
      "epoch:996 , loss:0.014634545892477036, accracy:0.763\n",
      "epoch:997 , loss:0.019118469208478928, accracy:0.752\n",
      "epoch:998 , loss:0.01144345011562109, accracy:0.774\n",
      "epoch:999 , loss:0.010708312503993511, accracy:0.776\n"
     ]
    }
   ],
   "source": [
    "model = GCN(hidden_channels=16)\n",
    "criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\n",
    "opt = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)  # Define optimizer.\n",
    "data=dataset[0]\n",
    "\n",
    "for i in range(1000):\n",
    "    out = model.forward(data.x, data.edge_index)\n",
    "    loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
    "    opt.zero_grad()\n",
    "    loss.backward()\n",
    "    opt.step()\n",
    "\n",
    "    pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "    test_correct = pred[data.test_mask] == data.y[data.test_mask]  # Check against ground-truth labels.\n",
    "    test_acc = int(test_correct.sum()) / int(data.test_mask.sum())  # Derive ratio of correct predictions.\n",
    "\n",
    "    print(\"epoch:{} , loss:{}, accracy:{}\".format(i,loss,test_acc))"
   ]
  }
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